The rest of the network includes two stacked bi-directional LSTM recurrent layers (each with 128 units in both forward and backward directions), no temporal pooling, two feed-forward layers (128 units, tanh activation) and a final classification layer (2 units, softmax activation). Speaker embedding uses a slightly wider (512 units instead of 128) and deeper (3 recurrent layers instead of 2) network, relies on x-vector-like statistical temporal pooling [1]} and has been trained on shorter 500ms audio chunks. More details on the training process (number of epochs, optimizer, etc.) can be found directly in the associated configuration files, available from pyannote.audio repository.
